Mobile wireless communication devices such as cellular telephones, handheld PDAs, and the like, typically operate in a multi-access radio environment, for example involving communication between a base station or access point and plural mobile wireless communication devices communicatively coupled therewith, such as in a cellular network. Communication may comprise voice communication, data communication such as packet-switched communication, or the like, and may be subject to various requirements such as bandwidth or quality of service (QoS) requirements. Providing adequate levels of service in terrestrial environments can be challenging, for example due to the presence of radio interference, channel fading, and the like. Channel fading relates, for example, to attenuation of radio signals, such as carrier-modulated signals, which may be variable in time, space, radio frequency, or a combination thereof, or the like. Fading may be caused by several phenomena such as multipath or shadowing.
One approach to combat fading is to employ one or more diversity techniques. For example, when radio channel conditions are time-varying, multiuser diversity in a multi-access system may be achieved through opportunistic scheduling. Opportunistic scheduling generally involves monitoring channel qualities between the base station and one or more mobile wireless communication devices, and, at a given time, only allowing communication to occur over the best quality channels. Assuming that channel qualities vary substantially independently, there is a high probability that at least some channels will be of high quality at a given time. By using only these channels, improved spectrum utilization can be achieved for the overall system. Opportunistic scheduling has been proposed, for example, for use in the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard.
Practical implementations of schedulers such as opportunistic schedulers may utilize channel fading models for monitoring and predicting channel quality. For example, a two-state Markov chain stochastic channel model is proposed in “Frequency Domain Packet Scheduling Under Fractional Load for the UTRAN LTE Downlink,” by A. Pokhariyal, G. Monghal, K. Pedersen, P. Morgensen, I. Kovacs, C. Rosa, and T. Kolding, in Proceedings of the 65th IEEE Vehicular Technology Conference, April 2007, pp. 699-703. As another example, “Minimum-Energy Band-Limited Predictor With Dynamic Subspace Selection for Time-Variant Flat-Fading Channels,” by T. Zemen, C. Mecklenbrauker, F. Kaltenberger and B. Fleury, in IEEE Transactions on Signal Processing, September 2007, Volume 55, No. 9, pp. 4534-4548, discloses a channel prediction algorithm for wireless channels based on Slepian Sequences. Channel quality prediction may be useful when there is a delay between obtaining channel quality estimates and making channel use decisions based on those channel quality estimates.
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